Editorial Reviews:
Product Description
A Mathematical Primer for Social Statistics: Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. It is, however, surprising how far one can go with a relatively modest mathematical background. The proposed monograph aims to provide that background, introducing the areas of mathematics that are most centrally important to applied social statistics: matrices, linear algebra, and vector geometry; basic differential and integral calculus, including multivariable and matrix calculus, and the application of calculus to optimization problems; and probability and estimation, including the basics of probability theory, discrete and continuous random variables, commonly encountered statistical distributions, principles of estimation, the method of maximum likelihood and the basics of Bayesian inference.It would be advantageous to cover all these topics in a single book.The expected readership includes advanced undergrads, graduate students, and researchers in the social sciences who wish to learn and use relatively advanced statistical methods, especially regression type of analysis, but whose mathematical preparation for this work is insufficient. There should be a potentially large market. The title should be able to stand alone or bundled together with our existing numbers in two ways: it could go together with many of the volumes on regression analysis, and it could also help fill the gap in foundation with our existing numbers on matrix algebra and calculus. I expect it will do better in the former role of bundling together with existing volumes on regression.
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